Predicting the Peaking of Holiday Traffic (Victoria Day Example)
Bibliographic record
Abstract
In developed and fast developing countries, rising standards of living and the trend towards shorter working hours have significantly changed people's traveling behaviour. Literature reported that, during holiday periods, there were usually substantial increases in traffic volumes on highways. A clear understanding of the holiday traffic characteristics and a reasonable prediction of the upcoming high traffic volumes would significantly benefit both researchers and practitioners who are responsible for the planning, design, operation, and management of transportation networks. However, research focusing on holiday traffic has been minimal to date. The existing traffic prediction methods mainly aim on the flow during regular times. This paper is intended to first investigate the variation characteristics of holiday traffic. Then, the potential prediction errors using popular predicting methods such as time series are discussed. At last, a non-parametric method is proposed to predict the holiday traffic for different types of highways. On the basis of traffic volume data from major highways in Alberta, Canada, it is found that the performances of the proposed method are consistent and reasonable for different holiday periods and various types of roads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".